Guidance for a causal comparative effectiveness analysis emulating a target trial based on big real world evidence: when to start statin treatment-Reference-Cited by-同舟云学术

Guidance for a causal comparative effectiveness analysis emulating a target trial based on big real world evidence: when to start statin treatment

Published:2019-09 Issue:12 Volume:8 Page:1013-1025
ISSN:2042-6305
Container-title:Journal of Comparative Effectiveness Research
language:en
Short-container-title:Journal of Comparative Effectiveness Research

Author:

Kuehne Felicitas¹,Jahn Beate¹,Conrads-Frank Annette¹,Bundo Marvin¹,Arvandi Marjan¹,Endel Florian²,Popper Niki²³⁴,Endel Gottfried⁵,Urach Christoph⁴,Gyimesi Michael⁶,Murray Eleanor J⁷⁸,Danaei Goodarz⁸⁹,Gaziano Thomas A¹⁰¹¹,Pandya Ankur¹⁰,Siebert Uwe¹¹⁰¹²¹³

Affiliation:

1. Department of Public Health, Health Services Research & Health Technology Assessment, Institute of Public Health, Medical Decision Making & Health Technology Assessment, UMIT – University for Health Sciences, Medical Informatics & Technology, Hall iT, Austria

2. DEXHELPP, Vienna, Austria

3. TU Wien, Research Unit of Information and Software Engineering, Austria

4. dwh GmbH, Simulation Services & Technical Solutions, Austria

5. Department EWG, Main Association of Austrian Social Security Institutions, Vienna, Austria

6. Austrian Public Health Institute, Austria

7. Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA

8. Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA

9. Department of Global Health & Population, Harvard TH Chan School of Public Health, Boston, MA 02115, USA

10. Department of Health Policy & Management, Center for Health Decision Science, Harvard TH Chan School of Public Health, Boston, MA 02115, USA

11. Division of Cardiovascular Medicine, Brigham & Women’s Hospital, Boston, MA 02115, USA

12. Cardiovascular Research Program, Institute for Technology Assessment & Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA

13. Division of Health Technology Assessment & Bioinformatics, ONCOTYROL – Center for Personalized Cancer Medicine, Innsbruck, Austria

Abstract

Aim: The aim of this project is to describe a causal (counterfactual) approach for analyzing when to start statin treatment to prevent cardiovascular disease using real-world evidence. Methods: We use directed acyclic graphs to operationalize and visualize the causal research question considering selection bias, potential time-independent and time-dependent confounding. We provide a study protocol following the ‘target trial’ approach and describe the data structure needed for the causal assessment. Conclusion: The study protocol can be applied to real-world data, in general. However, the structure and quality of the database play an essential role for the validity of the results, and database-specific potential for bias needs to be explicitly considered.

Publisher

Future Medicine Ltd

Subject

Health Policy

Link

https://www.futuremedicine.com/doi/pdf/10.2217/cer-2018-0103

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